河北坝上高原如意河流域风积沙厚度空间展布预测方法

Prediction methods of spatial distribution of aeolian sand in Ruyi River Basin of Bashang Plateau,Hebei Province

不同的空间插值方法会对风积沙厚度空间分布预测精度产生重要影响。基于河北承德坝上高原东部如意河流域中游152组风积沙厚度数据,应用径向基函数人工神经网络(RBF-ANN)插值方法探究如意河流域中游风积沙厚度空间分布特征,并与地统计插值方法中的不同模型函数、确定性插值中的不同插值方法进行预测误差和计算结果对比分析。结果表明,在地统计插值方法中,经典贝叶斯克里金插值-幂半变异函数(EBK-Power)的插值效果最佳;在确定性插值方法中,径向基函数(RBF)插值效果最佳;RBF-ANN插值较EBK-Power和RBF方法在平均绝对误差(MAE)上的改进大于30%,在均方根误差(RMSE)上的改进大于20%,RBF-ANN插值更适用于如意河流域风积沙厚度空间分布预测。

Different spatial interpolation methods will have an important influence on the prediction accuracy of the spatial distribution of aeolian sand thickness.Based on the data of 152 groups of aeolian sand thickness in the middle reaches of Ruyi River Basin in the east of Bashang Plateau,Chengde,this paper used the Radial Basis Function-Artificial Neural Network(RBF-ANN)interpolation method to explore the spatial distribution characteristics of aeolian sand thickness in this area,and compared the prediction error and calculation results among different model functions in geostatistical analyst method and different interpolation methods in deterministic interpolation method.The results showed that among the geostatistical analyst methods,the classical EBK-Power has the best interpolation effect and among the deterministic interpolation methods the RBF interpolation is the best.Compared with EBK-Power and RBF interpolation,RBF-ANN interpolation improves the mean absolute error by more than 30%,and the improvement on the root mean square error was more than 20%,so it was concluded that RBF-ANN interpolation was more suitable for predicting the spatial distribution of aeolian sand thickness in Ruyi River Basin.